| # AMI | |
| This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's | |
| headset and lapel microphones, and also 2 array microphones containing 8 channels each. | |
| We pool data in the following 4 ways and train a single model on the pooled data: | |
| (i) individual headset microphone (IHM) | |
| (ii) IHM with simulated reverb | |
| (iii) Single distant microphone (SDM) | |
| (iv) GSS-enhanced array microphones | |
| Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled | |
| data. Here are the statistics of the combined training data: | |
| ```python | |
| >>> cuts_train.describe() | |
| Cuts count: 1222053 | |
| Total duration (hh:mm:ss): 905:00:28 | |
| Speech duration (hh:mm:ss): 905:00:28 (99.9%) | |
| Duration statistics (seconds): | |
| mean 2.7 | |
| std 2.8 | |
| min 0.0 | |
| 25% 0.6 | |
| 50% 1.6 | |
| 75% 3.8 | |
| 99% 12.3 | |
| 99.5% 13.9 | |
| 99.9% 18.4 | |
| max 36.8 | |
| ``` | |
| **Note:** This recipe additionally uses [GSS](https://github.com/desh2608/gss) for enhancement | |
| of far-field array microphones, but this is optional (see `prepare.sh` for details). | |
| ## Performance Record | |
| ### pruned_transducer_stateless7 | |
| The following are decoded using `modified_beam_search`: | |
| | Evaluation set | dev WER | test WER | | |
| |--------------------------|------------|---------| | |
| | IHM | 19.23 | 18.06 | | |
| | SDM | 31.16 | 32.61 | | |
| | MDM (GSS-enhanced) | 22.08 | 23.03 | | |
| See [RESULTS](/egs/ami/ASR/RESULTS.md) for details. | |